{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "592a82a5-d3f3-4d47-92ac-8ab13ab55cc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from uniphm.data import Dataset, TurbofanRulLabeler\n",
    "from uniphm.data.loader.CMAPSSLoader import CMAPSSLoader\n",
    "from uniphm.data.process.array.NormalizationProcessor import NormalizationProcessor\n",
    "from uniphm.engine.Evaluator import Evaluator\n",
    "from uniphm.engine.metric.MSE import MSE\n",
    "from uniphm.engine.metric.MAE import MAE\n",
    "from uniphm.engine.metric.PHM2008Score import PHM2008Score\n",
    "from uniphm.engine.metric.PHM2012Score import PHM2012Score\n",
    "from uniphm.engine.metric.PercentError import PercentError\n",
    "from uniphm.engine.metric.RMSE import RMSE\n",
    "from uniphm.engine.tester.BaseTester import BaseTester\n",
    "from uniphm.engine.trainer.BaseTrainer import BaseTrainer\n",
    "from uniphm.model.classic.Transfromer import TransformerEncoderModel\n",
    "from uniphm.engine.callback.CheckGradientsCallback import CheckGradientsCallback\n",
    "from uniphm.engine.callback.EarlyStoppingCallback import EarlyStoppingCallback\n",
    "from uniphm.engine.callback.TensorBoardCallback import TensorBoardCallback\n",
    "from uniphm.util.Cache import Cache\n",
    "from uniphm.util.Plotter import Plotter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1c18c11f-5176-4395-94ec-3e0bce2b5682",
   "metadata": {},
   "outputs": [],
   "source": [
    "cache_model = False\n",
    "Plotter.DPI = 40\n",
    "Plotter.SIZE = (4, 2.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4289339f-b037-4244-a9da-7e317d8d8917",
   "metadata": {},
   "source": [
    "# 基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd88d7a2-3305-427a-ba73-1f46ed6b9c98",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[DEBUG   17:24:22]  \n",
      "[DataLoader]  Root directory: D:\\data\\dataset\\CMAPSSData\n",
      "\t✓ FD001_RUL, location: D:\\data\\dataset\\CMAPSSData\\RUL_FD001.txt\n",
      "\t✓ FD002_RUL, location: D:\\data\\dataset\\CMAPSSData\\RUL_FD002.txt\n",
      "\t✓ FD003_RUL, location: D:\\data\\dataset\\CMAPSSData\\RUL_FD003.txt\n",
      "\t✓ FD004_RUL, location: D:\\data\\dataset\\CMAPSSData\\RUL_FD004.txt\n",
      "\t✓ FD001_test, location: D:\\data\\dataset\\CMAPSSData\\test_FD001.txt\n",
      "\t✓ FD002_test, location: D:\\data\\dataset\\CMAPSSData\\test_FD002.txt\n",
      "\t✓ FD003_test, location: D:\\data\\dataset\\CMAPSSData\\test_FD003.txt\n",
      "\t✓ FD004_test, location: D:\\data\\dataset\\CMAPSSData\\test_FD004.txt\n",
      "\t✓ FD001_train, location: D:\\data\\dataset\\CMAPSSData\\train_FD001.txt\n",
      "\t✓ FD002_train, location: D:\\data\\dataset\\CMAPSSData\\train_FD002.txt\n",
      "\t✓ FD003_train, location: D:\\data\\dataset\\CMAPSSData\\train_FD003.txt\n",
      "\t✓ FD004_train, location: D:\\data\\dataset\\CMAPSSData\\train_FD004.txt\n"
     ]
    }
   ],
   "source": [
    "data_loader = CMAPSSLoader('D:\\\\data\\\\dataset\\\\CMAPSSData')\n",
    "sensor = ['T24', 'T30', 'T50', 'P30', 'Nf', 'Nc', 'Ps30', 'phi', 'NRF', 'NRc', 'BPR', 'htBleed', 'W31', 'W32']\n",
    "sensor_norm = ['norm_' + s for s in sensor]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d0fd06-5582-4fe9-b043-a6f9b50c47de",
   "metadata": {},
   "source": [
    "# 发动机数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "406ebd0d-efac-48a0-9613-a5998ac5231b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:32:17]  [DataLoader]  -> Loading data entity: FD001_train_1\n",
      "[INFO    17:32:17]  [DataLoader]  ✓ Successfully loaded: FD001_train_1\n"
     ]
    }
   ],
   "source": [
    "turbofan = data_loader('FD001_train_1', include=sensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acceb803-cbf4-4182-a82f-27fdffa498aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 320x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'default'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plotter.SIZE = (8, 5)\n",
    "Plotter.entity(turbofan, sensor_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9666a27a-7a92-4e8c-910b-96773511928c",
   "metadata": {},
   "source": [
    "# 配置标签器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a94883c-834c-4a9c-8e95-118daac9b458",
   "metadata": {},
   "outputs": [],
   "source": [
    "labeler_all_sample = TurbofanRulLabeler(window_size=30, window_step=1, max_rul=130)\n",
    "labeler_last_sample = TurbofanRulLabeler(window_size=30, window_step=1, max_rul=130, last_sample=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c03dbf64-a37d-4ad4-9b9e-ffe127556648",
   "metadata": {},
   "source": [
    "# 加载训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a847a5b1-5d70-48e3-9949-9e82e6602fb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_1\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_1\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_2\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_2\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_3\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_3\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_4\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_4\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_5\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_5\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_6\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_6\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_7\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_7\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_8\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_8\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_9\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_9\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_10\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_10\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_11\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_11\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_12\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_12\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_13\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_13\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_14\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_14\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_15\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_15\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_16\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_16\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_17\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_17\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_18\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_18\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_19\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_19\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_20\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_20\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_21\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_21\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_22\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_22\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_23\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_23\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_24\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_24\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_25\n",
      "[INFO    17:24:24]  [DataLoader]  ✓ Successfully loaded: FD001_train_25\n",
      "[INFO    17:24:24]  [DataLoader]  -> Loading data entity: FD001_train_26\n",
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      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_1', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_1'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_2', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_2'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_3', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_3'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_4', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_4'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_5', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_5'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_6', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_6'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_7', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_7'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_8', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_8'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_9', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_9'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_10', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_10'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_11', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_11'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_12', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_12'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_13', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_13'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_14', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_14'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_15', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_15'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_16', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_16'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_17', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_17'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_18', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_18'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_19', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_19'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_20', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_20'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_21', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:24]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_21'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_22', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_22'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_23', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_23'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_24', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_24'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_25', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_25'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_26', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_26'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_27', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_27'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_28', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_28'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_29', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_29'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_30', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_30'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_31', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_31'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_32', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_32'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_33', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_33'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_34', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_34'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_35', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_35'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_36', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_36'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_37', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_37'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_38', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_38'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_39', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_39'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_40', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_40'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_41', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_41'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_42', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_42'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_43', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_43'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_44', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_44'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_45', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_45'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_46', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_46'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_47', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_47'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_48', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_48'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_49', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_49'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_50', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_50'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_51', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_51'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_52', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_52'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_53', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_53'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_54', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_54'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_55', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_55'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_56', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_56'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_57', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_57'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_58', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_58'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_59', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_59'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_60', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_60'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_61', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_61'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_62', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_62'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_63', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_63'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_64', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_64'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_65', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_65'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_66', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_66'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_67', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_67'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_68', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_68'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_69', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_69'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_70', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_70'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_71', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_71'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_72', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_72'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_73', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_73'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_74', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_74'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_75', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_75'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_76', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_76'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_77', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_77'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_78', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_78'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_79', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_79'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_80', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_80'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_81', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_81'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_82', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_82'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_83', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_83'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_84', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_84'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_85', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_85'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_86', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_86'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_87', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_87'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_88', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_88'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_89', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_89'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_90', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_90'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_91', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_91'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_92', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_92'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_93', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_93'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_94', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_94'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_95', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_95'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_96', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_96'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_97', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_97'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_98', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_98'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_99', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_99'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_train_100', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:25]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_train_100'\n"
     ]
    }
   ],
   "source": [
    "turbofans_train = data_loader.batch_load('FD001_train', include=sensor)\n",
    "train_set = Dataset()\n",
    "for turbofan in turbofans_train:\n",
    "    train_set.append_entity(labeler_all_sample(turbofan, sensor_norm))\n",
    "train_set.name = 'FD001_train'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b855820e-bb44-48c3-aa41-17e771e765ca",
   "metadata": {},
   "source": [
    "# 加载测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6859b8af-7a12-40a9-8dc9-8d7fa0a940eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_1\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_1\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_2\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_2\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_3\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_3\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_4\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_4\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_5\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_5\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_6\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_6\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_7\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_7\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_8\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_8\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_9\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_9\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_10\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_10\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_11\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_11\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_12\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_12\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_13\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_13\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_14\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_14\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_15\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_15\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_16\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_16\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_17\n",
      "[INFO    17:24:25]  [DataLoader]  ✓ Successfully loaded: FD001_test_17\n",
      "[INFO    17:24:25]  [DataLoader]  -> Loading data entity: FD001_test_18\n",
      "[INFO    17:24:26]  [DataLoader]  ✓ Successfully loaded: FD001_test_18\n",
      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_19\n",
      "[INFO    17:24:26]  [DataLoader]  ✓ Successfully loaded: FD001_test_19\n",
      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_20\n",
      "[INFO    17:24:26]  [DataLoader]  ✓ Successfully loaded: FD001_test_20\n",
      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_21\n",
      "[INFO    17:24:26]  [DataLoader]  ✓ Successfully loaded: FD001_test_21\n",
      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_22\n",
      "[INFO    17:24:26]  [DataLoader]  ✓ Successfully loaded: FD001_test_22\n",
      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_23\n",
      "[INFO    17:24:26]  [DataLoader]  ✓ Successfully loaded: FD001_test_23\n",
      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_24\n",
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      "[INFO    17:24:26]  [DataLoader]  -> Loading data entity: FD001_test_25\n",
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      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_1', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_1'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_1', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_1'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_2', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_2'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_2', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_2'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_3', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_3'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_3', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_3'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_4', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_4'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_4', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_4'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_5', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_5'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_5', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_5'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_6', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_6'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_6', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_6'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_7', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_7'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_7', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_7'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_8', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_8'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_8', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_8'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_9', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_9'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_9', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_9'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_10', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_10'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_10', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_10'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_11', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_11'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_11', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_11'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_12', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_12'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_12', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_12'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_13', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_13'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_13', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_13'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_14', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_14'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_14', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_14'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_15', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_15'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_15', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_15'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_16', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_16'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_16', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_16'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_17', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_17'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_17', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_17'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_18', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_18'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_18', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_18'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_19', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_19'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_19', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_19'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_20', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_20'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_20', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_20'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_21', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_21'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_21', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_21'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_22', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_22'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_22', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_22'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_23', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_23'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_23', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_23'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_24', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_24'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_24', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_24'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_25', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_25'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_25', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_25'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_26', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_26'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_26', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_26'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_27', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_27'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_27', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_27'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_28', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_28'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_28', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_28'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_29', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_29'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_29', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_29'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_30', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_30'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_30', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_30'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_31', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_31'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_31', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_31'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_32', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_32'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_32', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_32'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_33', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_33'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_33', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_33'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_34', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_34'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_34', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_34'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_35', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_35'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_35', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_35'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_36', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_36'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_36', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_36'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_37', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_37'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_37', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_37'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_38', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_38'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_38', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_38'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_39', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_39'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_39', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_39'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_40', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_40'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_40', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_40'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_41', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_41'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_41', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_41'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_42', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_42'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_42', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_42'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_43', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_43'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_43', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_43'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_44', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_44'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_44', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_44'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_45', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_45'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_45', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_45'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_46', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_46'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_46', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_46'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_47', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_47'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_47', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_47'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_48', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_48'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_48', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_48'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_49', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_49'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_49', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_49'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_50', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_50'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_50', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_50'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_51', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_51'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_51', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_51'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_52', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_52'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_52', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_52'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_53', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_53'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_53', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_53'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_54', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_54'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_54', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_54'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_55', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_55'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_55', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_55'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_56', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_56'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_56', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_56'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_57', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_57'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_57', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_57'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_58', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_58'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_58', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_58'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_59', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_59'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_59', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_59'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_60', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_60'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_60', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_60'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_61', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_61'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_61', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_61'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_62', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_62'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_62', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_62'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_63', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_63'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_63', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_63'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_64', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_64'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_64', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_64'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_65', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_65'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_65', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_65'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_66', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_66'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_66', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_66'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_67', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_67'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_67', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_67'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_68', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_68'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_68', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_68'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_69', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_69'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_69', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_69'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_70', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_70'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_70', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_70'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_71', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_71'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_71', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_71'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_72', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_72'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_72', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_72'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_73', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_73'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_73', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_73'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_74', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_74'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_74', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_74'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_75', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_75'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_75', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_75'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_76', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_76'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_76', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_76'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_77', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_77'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_77', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_77'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_78', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_78'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_78', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_78'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_79', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_79'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_79', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_79'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_80', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_80'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_80', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_80'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_81', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_81'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_81', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_81'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_82', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_82'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_82', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_82'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_83', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_83'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_83', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:26]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_83'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_84', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_84'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_84', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_84'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_85', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_85'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_85', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_85'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_86', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_86'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_86', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_86'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_87', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_87'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_87', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_87'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_88', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_88'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_88', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_88'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_89', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_89'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_89', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_89'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_90', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_90'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_90', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_90'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_91', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_91'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_91', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_91'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_92', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_92'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_92', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_92'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_93', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_93'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_93', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_93'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_94', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_94'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_94', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_94'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_95', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_95'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_95', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_95'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_96', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_96'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_96', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_96'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_97', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_97'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_97', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_97'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_98', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_98'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_98', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_98'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_99', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_99'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_99', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_99'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_100', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_100'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  -> Generating dataset for entity: 'FD001_test_100', key: '['norm_T24', 'norm_T30', 'norm_T50', 'norm_P30', 'norm_Nf', 'norm_Nc', 'norm_Ps30', 'norm_phi', 'norm_NRF', 'norm_NRc', 'norm_BPR', 'norm_htBleed', 'norm_W31', 'norm_W32']'\n",
      "[INFO    17:24:27]  [TurbofanRulLabeler]  ✓ Finished labeling for entity: 'FD001_test_100'\n"
     ]
    }
   ],
   "source": [
    "turbofan_test = data_loader.batch_load('FD001_test', include=sensor)\n",
    "test_set_last_sample = Dataset()\n",
    "test_set_all_sample = Dataset()\n",
    "for turbofan in turbofan_test:\n",
    "    test_set_last_sample.append_entity(labeler_last_sample(turbofan, sensor_norm))\n",
    "    test_set_all_sample.append_entity(labeler_all_sample(turbofan, sensor_norm))\n",
    "test_set_last_sample.name = 'FD001_test'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4063e12b-39c8-4531-91fe-f3f5769e8358",
   "metadata": {},
   "source": [
    "# 标签归一化训练能显著降低梯度消失的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfb16df0-fac9-42ac-b9a6-f7b1ec17b862",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm = NormalizationProcessor(arr_min=0, arr_max=130, mode='[0,1]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49c1066-0312-49e4-8096-cde03532ecec",
   "metadata": {},
   "source": [
    "# 配置测试算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e60683c8-a4f8-4960-bd2f-e6e087acc4f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_config = {\n",
    "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
    "    'dtype': torch.float32,\n",
    "    'norm': norm\n",
    "}\n",
    "tester = BaseTester(config=test_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e9d62f8-f799-4601-9c70-346f02250280",
   "metadata": {},
   "source": [
    "# 配置训练算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "381a7057-0663-4e44-8313-533bf327c1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_config = {\n",
    "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
    "    'dtype': torch.float32,\n",
    "    'epochs': 120,\n",
    "    'batch_size': 512,\n",
    "    'lr': 0.01,\n",
    "    'weight_decay': 0.01,\n",
    "    'criterion': nn.MSELoss(),\n",
    "    'norm': norm,\n",
    "    'callbacks': [\n",
    "        EarlyStoppingCallback(patience=20,\n",
    "                              val_set=train_set,\n",
    "                              metric=RMSE(),\n",
    "                              tester=tester),\n",
    "        CheckGradientsCallback(),\n",
    "        TensorBoardCallback()]\n",
    "}\n",
    "trainer = BaseTrainer(config=train_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccb3073f-fad3-4fd2-9627-1e6075b1fcb9",
   "metadata": {},
   "source": [
    "# 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "401d532b-d049-44fc-9299-cf59de848a47",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:24:27]  \n",
      "[Trainer]  Start training by BaseTrainer:\n",
      "\ttraining set: FD001_train\n",
      "\tdevice: cuda\n",
      "\tdtype: torch.float32\n",
      "\tepochs: 120\n",
      "\tbatch_size: 512\n",
      "\tlr: 0.01\n",
      "\tweight_decay: 0.01\n",
      "\tcriterion: MSELoss()\n",
      "\tnorm: NormalizationProcessor(min=0, max=130, mean=None, std=None, mode=[0,1])\n",
      "\tcallbacks: [EarlyStoppingCallback, CheckGradientsCallback, TensorBoardCallback]\n",
      "\toptimizer: Adam\n",
      "[DEBUG   17:24:28]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.4888\n",
      "[INFO    17:24:29]  [BaseTrainer]  Epoch [1/120], MSELoss:13141.5850\n",
      "[DEBUG   17:24:29]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 44.8702\n",
      "[DEBUG   17:24:29]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[WARNING 17:24:29]  [CheckGradients] output_linear.weight gradient is very large: 1.14e+03\n",
      "[WARNING 17:24:29]  [CheckGradients] output_linear.bias gradient is very large: 1.52e+03\n",
      "[INFO    17:24:30]  [BaseTrainer]  Epoch [2/120], MSELoss:1932.9805\n",
      "[DEBUG   17:24:31]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.6874\n",
      "[DEBUG   17:24:31]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:24:32]  [BaseTrainer]  Epoch [3/120], MSELoss:1916.3443\n",
      "[DEBUG   17:24:32]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.3530\n",
      "[WARNING 17:24:32]  [CheckGradients] output_linear.bias gradient is very large: 1.23e+03\n",
      "[INFO    17:24:33]  [BaseTrainer]  Epoch [4/120], MSELoss:1909.4386\n",
      "[DEBUG   17:24:34]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.4247\n",
      "[DEBUG   17:24:34]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[WARNING 17:24:34]  [CheckGradients] output_linear.weight gradient is very large: 1.35e+03\n",
      "[WARNING 17:24:34]  [CheckGradients] output_linear.bias gradient is very large: 1.98e+03\n",
      "[INFO    17:24:35]  [BaseTrainer]  Epoch [5/120], MSELoss:1903.1805\n",
      "[DEBUG   17:24:35]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.4370\n",
      "[DEBUG   17:24:35]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:24:36]  [BaseTrainer]  Epoch [6/120], MSELoss:1902.0475\n",
      "[DEBUG   17:24:37]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.3466\n",
      "[INFO    17:24:38]  [BaseTrainer]  Epoch [7/120], MSELoss:1897.5911\n",
      "[DEBUG   17:24:38]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.3466\n",
      "[DEBUG   17:24:38]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:24:39]  [BaseTrainer]  Epoch [8/120], MSELoss:1901.4728\n",
      "[DEBUG   17:24:40]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 44.0403\n",
      "[DEBUG   17:24:40]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:24:41]  [BaseTrainer]  Epoch [9/120], MSELoss:1895.6406\n",
      "[DEBUG   17:24:41]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.9356\n",
      "[DEBUG   17:24:41]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:24:43]  [BaseTrainer]  Epoch [10/120], MSELoss:1895.0750\n",
      "[DEBUG   17:24:43]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.8496\n",
      "[DEBUG   17:24:43]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[WARNING 17:24:43]  [CheckGradients] output_linear.bias gradient is very large: 1.18e+03\n",
      "[INFO    17:24:44]  [BaseTrainer]  Epoch [11/120], MSELoss:1930.6977\n",
      "[DEBUG   17:24:44]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.5437\n",
      "[DEBUG   17:24:44]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[WARNING 17:24:44]  [CheckGradients] output_linear.weight gradient is very large: 2.06e+03\n",
      "[WARNING 17:24:44]  [CheckGradients] output_linear.bias gradient is very large: 3.36e+03\n",
      "[INFO    17:24:46]  [BaseTrainer]  Epoch [12/120], MSELoss:1910.1465\n",
      "[DEBUG   17:24:46]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.5056\n",
      "[DEBUG   17:24:46]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[INFO    17:24:47]  [BaseTrainer]  Epoch [13/120], MSELoss:1887.2819\n",
      "[DEBUG   17:24:47]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.7500\n",
      "[DEBUG   17:24:47]  [EarlyStopping]  No improvement for [7/20] epochs\n",
      "[INFO    17:24:49]  [BaseTrainer]  Epoch [14/120], MSELoss:1859.5446\n",
      "[DEBUG   17:24:49]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 43.2948\n",
      "[WARNING 17:24:49]  [CheckGradients] output_linear.bias gradient is very large: 1.54e+03\n",
      "[INFO    17:24:50]  [BaseTrainer]  Epoch [15/120], MSELoss:1866.7961\n",
      "[DEBUG   17:24:51]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 40.1015\n",
      "[INFO    17:24:52]  [BaseTrainer]  Epoch [16/120], MSELoss:665.2435\n",
      "[DEBUG   17:24:52]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 20.8832\n",
      "[INFO    17:24:53]  [BaseTrainer]  Epoch [17/120], MSELoss:386.6252\n",
      "[DEBUG   17:24:54]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 17.8861\n",
      "[INFO    17:24:55]  [BaseTrainer]  Epoch [18/120], MSELoss:313.4160\n",
      "[DEBUG   17:24:55]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 15.5614\n",
      "[INFO    17:24:56]  [BaseTrainer]  Epoch [19/120], MSELoss:260.8248\n",
      "[DEBUG   17:24:57]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.9195\n",
      "[INFO    17:24:58]  [BaseTrainer]  Epoch [20/120], MSELoss:259.8183\n",
      "[DEBUG   17:24:58]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.5772\n",
      "[INFO    17:25:00]  [BaseTrainer]  Epoch [21/120], MSELoss:221.8555\n",
      "[DEBUG   17:25:00]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.8171\n",
      "[DEBUG   17:25:00]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:01]  [BaseTrainer]  Epoch [22/120], MSELoss:226.1100\n",
      "[DEBUG   17:25:01]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.6625\n",
      "[DEBUG   17:25:01]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:25:03]  [BaseTrainer]  Epoch [23/120], MSELoss:213.5692\n",
      "[DEBUG   17:25:03]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.8803\n",
      "[WARNING 17:25:03]  [CheckGradients] output_linear.weight gradient is very large: 1.02e+03\n",
      "[WARNING 17:25:03]  [CheckGradients] output_linear.bias gradient is very large: 1.51e+03\n",
      "[INFO    17:25:04]  [BaseTrainer]  Epoch [24/120], MSELoss:211.5845\n",
      "[DEBUG   17:25:05]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.7207\n",
      "[DEBUG   17:25:05]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:06]  [BaseTrainer]  Epoch [25/120], MSELoss:207.6501\n",
      "[DEBUG   17:25:06]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.9208\n",
      "[DEBUG   17:25:06]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:25:07]  [BaseTrainer]  Epoch [26/120], MSELoss:201.1744\n",
      "[DEBUG   17:25:08]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 15.8985\n",
      "[DEBUG   17:25:08]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:25:09]  [BaseTrainer]  Epoch [27/120], MSELoss:199.2838\n",
      "[DEBUG   17:25:09]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.0834\n",
      "[DEBUG   17:25:09]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:25:10]  [BaseTrainer]  Epoch [28/120], MSELoss:206.0432\n",
      "[DEBUG   17:25:11]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 16.8436\n",
      "[DEBUG   17:25:11]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:25:12]  [BaseTrainer]  Epoch [29/120], MSELoss:206.1945\n",
      "[DEBUG   17:25:12]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.9636\n",
      "[DEBUG   17:25:12]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[WARNING 17:25:12]  [CheckGradients] output_linear.weight gradient is very large: 1.26e+03\n",
      "[WARNING 17:25:12]  [CheckGradients] output_linear.bias gradient is very large: 1.82e+03\n",
      "[INFO    17:25:14]  [BaseTrainer]  Epoch [30/120], MSELoss:206.3160\n",
      "[DEBUG   17:25:14]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 17.1982\n",
      "[DEBUG   17:25:14]  [EarlyStopping]  No improvement for [7/20] epochs\n",
      "[WARNING 17:25:14]  [CheckGradients] output_linear.weight gradient is very large: 1.32e+03\n",
      "[WARNING 17:25:14]  [CheckGradients] output_linear.bias gradient is very large: 1.82e+03\n",
      "[INFO    17:25:15]  [BaseTrainer]  Epoch [31/120], MSELoss:207.1892\n",
      "[DEBUG   17:25:15]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.4558\n",
      "[INFO    17:25:17]  [BaseTrainer]  Epoch [32/120], MSELoss:188.9810\n",
      "[DEBUG   17:25:17]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 15.1147\n",
      "[DEBUG   17:25:17]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:18]  [BaseTrainer]  Epoch [33/120], MSELoss:194.8338\n",
      "[DEBUG   17:25:19]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.5180\n",
      "[DEBUG   17:25:19]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:25:20]  [BaseTrainer]  Epoch [34/120], MSELoss:192.6664\n",
      "[DEBUG   17:25:20]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.6379\n",
      "[DEBUG   17:25:20]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:25:21]  [BaseTrainer]  Epoch [35/120], MSELoss:191.3509\n",
      "[DEBUG   17:25:22]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 15.5644\n",
      "[DEBUG   17:25:22]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:25:23]  [BaseTrainer]  Epoch [36/120], MSELoss:190.6762\n",
      "[DEBUG   17:25:23]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.2521\n",
      "[INFO    17:25:24]  [BaseTrainer]  Epoch [37/120], MSELoss:194.9400\n",
      "[DEBUG   17:25:25]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 15.2023\n",
      "[DEBUG   17:25:25]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:26]  [BaseTrainer]  Epoch [38/120], MSELoss:188.9308\n",
      "[DEBUG   17:25:26]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.1556\n",
      "[INFO    17:25:28]  [BaseTrainer]  Epoch [39/120], MSELoss:188.3900\n",
      "[DEBUG   17:25:28]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.0118\n",
      "[WARNING 17:25:28]  [CheckGradients] output_linear.weight gradient is very large: 1.21e+03\n",
      "[WARNING 17:25:28]  [CheckGradients] output_linear.bias gradient is very large: 1.79e+03\n",
      "[INFO    17:25:29]  [BaseTrainer]  Epoch [40/120], MSELoss:193.3399\n",
      "[DEBUG   17:25:29]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.8525\n",
      "[DEBUG   17:25:29]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[WARNING 17:25:29]  [CheckGradients] output_linear.weight gradient is very large: 1.11e+03\n",
      "[WARNING 17:25:29]  [CheckGradients] output_linear.bias gradient is very large: 1.57e+03\n",
      "[INFO    17:25:31]  [BaseTrainer]  Epoch [41/120], MSELoss:191.9886\n",
      "[DEBUG   17:25:31]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.5072\n",
      "[DEBUG   17:25:31]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:25:32]  [BaseTrainer]  Epoch [42/120], MSELoss:189.5333\n",
      "[DEBUG   17:25:33]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.2697\n",
      "[DEBUG   17:25:33]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:25:34]  [BaseTrainer]  Epoch [43/120], MSELoss:193.1608\n",
      "[DEBUG   17:25:34]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.4465\n",
      "[DEBUG   17:25:34]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:25:35]  [BaseTrainer]  Epoch [44/120], MSELoss:182.5570\n",
      "[DEBUG   17:25:36]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.2225\n",
      "[DEBUG   17:25:36]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:25:37]  [BaseTrainer]  Epoch [45/120], MSELoss:196.8416\n",
      "[DEBUG   17:25:37]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.3400\n",
      "[DEBUG   17:25:37]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[INFO    17:25:38]  [BaseTrainer]  Epoch [46/120], MSELoss:188.3698\n",
      "[DEBUG   17:25:39]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.9069\n",
      "[INFO    17:25:40]  [BaseTrainer]  Epoch [47/120], MSELoss:176.9361\n",
      "[DEBUG   17:25:40]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.0086\n",
      "[DEBUG   17:25:40]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:42]  [BaseTrainer]  Epoch [48/120], MSELoss:182.5237\n",
      "[DEBUG   17:25:42]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.3518\n",
      "[DEBUG   17:25:42]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:25:43]  [BaseTrainer]  Epoch [49/120], MSELoss:174.5008\n",
      "[DEBUG   17:25:43]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.8506\n",
      "[INFO    17:25:45]  [BaseTrainer]  Epoch [50/120], MSELoss:170.2312\n",
      "[DEBUG   17:25:45]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.6914\n",
      "[DEBUG   17:25:45]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:46]  [BaseTrainer]  Epoch [51/120], MSELoss:172.4177\n",
      "[DEBUG   17:25:47]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.6779\n",
      "[INFO    17:25:48]  [BaseTrainer]  Epoch [52/120], MSELoss:175.6101\n",
      "[DEBUG   17:25:49]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.7851\n",
      "[DEBUG   17:25:49]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:50]  [BaseTrainer]  Epoch [53/120], MSELoss:166.9545\n",
      "[DEBUG   17:25:50]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.1782\n",
      "[INFO    17:25:52]  [BaseTrainer]  Epoch [54/120], MSELoss:174.5610\n",
      "[DEBUG   17:25:52]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.6840\n",
      "[DEBUG   17:25:52]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:25:54]  [BaseTrainer]  Epoch [55/120], MSELoss:169.1735\n",
      "[DEBUG   17:25:54]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.3352\n",
      "[DEBUG   17:25:54]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:25:56]  [BaseTrainer]  Epoch [56/120], MSELoss:161.0204\n",
      "[DEBUG   17:25:56]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.5927\n",
      "[DEBUG   17:25:56]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:25:58]  [BaseTrainer]  Epoch [57/120], MSELoss:163.0338\n",
      "[DEBUG   17:25:58]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.7974\n",
      "[DEBUG   17:25:58]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:26:00]  [BaseTrainer]  Epoch [58/120], MSELoss:156.2997\n",
      "[DEBUG   17:26:00]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.2594\n",
      "[DEBUG   17:26:00]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:26:01]  [BaseTrainer]  Epoch [59/120], MSELoss:161.6581\n",
      "[DEBUG   17:26:02]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.6997\n",
      "[DEBUG   17:26:02]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[WARNING 17:26:02]  [CheckGradients] output_linear.bias gradient is very large: 1.08e+03\n",
      "[INFO    17:26:03]  [BaseTrainer]  Epoch [60/120], MSELoss:155.3759\n",
      "[DEBUG   17:26:04]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.8341\n",
      "[INFO    17:26:05]  [BaseTrainer]  Epoch [61/120], MSELoss:159.0227\n",
      "[DEBUG   17:26:05]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.0561\n",
      "[DEBUG   17:26:05]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:26:07]  [BaseTrainer]  Epoch [62/120], MSELoss:150.7808\n",
      "[DEBUG   17:26:07]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.8422\n",
      "[DEBUG   17:26:07]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:26:08]  [BaseTrainer]  Epoch [63/120], MSELoss:161.8198\n",
      "[DEBUG   17:26:09]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.5573\n",
      "[DEBUG   17:26:09]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:26:10]  [BaseTrainer]  Epoch [64/120], MSELoss:153.3730\n",
      "[DEBUG   17:26:10]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.1171\n",
      "[DEBUG   17:26:10]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:26:11]  [BaseTrainer]  Epoch [65/120], MSELoss:145.8738\n",
      "[DEBUG   17:26:12]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.4792\n",
      "[DEBUG   17:26:12]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[WARNING 17:26:12]  [CheckGradients] output_linear.bias gradient is very large: 1.02e+03\n",
      "[INFO    17:26:13]  [BaseTrainer]  Epoch [66/120], MSELoss:141.9107\n",
      "[DEBUG   17:26:13]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.3076\n",
      "[INFO    17:26:15]  [BaseTrainer]  Epoch [67/120], MSELoss:142.0694\n",
      "[DEBUG   17:26:15]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.9996\n",
      "[DEBUG   17:26:15]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:26:16]  [BaseTrainer]  Epoch [68/120], MSELoss:141.5666\n",
      "[DEBUG   17:26:16]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.4047\n",
      "[DEBUG   17:26:16]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:26:18]  [BaseTrainer]  Epoch [69/120], MSELoss:151.4292\n",
      "[DEBUG   17:26:18]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.8561\n",
      "[DEBUG   17:26:18]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[WARNING 17:26:18]  [CheckGradients] output_linear.bias gradient is very large: 1.19e+03\n",
      "[INFO    17:26:19]  [BaseTrainer]  Epoch [70/120], MSELoss:142.1474\n",
      "[DEBUG   17:26:20]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.4113\n",
      "[DEBUG   17:26:20]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:26:21]  [BaseTrainer]  Epoch [71/120], MSELoss:149.8701\n",
      "[DEBUG   17:26:21]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.4102\n",
      "[DEBUG   17:26:21]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:26:23]  [BaseTrainer]  Epoch [72/120], MSELoss:136.6200\n",
      "[DEBUG   17:26:23]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.8054\n",
      "[DEBUG   17:26:23]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[WARNING 17:26:23]  [CheckGradients] output_linear.bias gradient is very large: 1.18e+03\n",
      "[INFO    17:26:24]  [BaseTrainer]  Epoch [73/120], MSELoss:140.6717\n",
      "[DEBUG   17:26:25]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 14.5065\n",
      "[DEBUG   17:26:25]  [EarlyStopping]  No improvement for [7/20] epochs\n",
      "[INFO    17:26:26]  [BaseTrainer]  Epoch [74/120], MSELoss:139.7046\n",
      "[DEBUG   17:26:26]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.3289\n",
      "[DEBUG   17:26:26]  [EarlyStopping]  No improvement for [8/20] epochs\n",
      "[INFO    17:26:28]  [BaseTrainer]  Epoch [75/120], MSELoss:143.0842\n",
      "[DEBUG   17:26:28]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.6456\n",
      "[DEBUG   17:26:28]  [EarlyStopping]  No improvement for [9/20] epochs\n",
      "[WARNING 17:26:28]  [CheckGradients] output_linear.bias gradient is very large: 1.26e+03\n",
      "[INFO    17:26:29]  [BaseTrainer]  Epoch [76/120], MSELoss:150.4959\n",
      "[DEBUG   17:26:30]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.9265\n",
      "[DEBUG   17:26:30]  [EarlyStopping]  No improvement for [10/20] epochs\n",
      "[INFO    17:26:32]  [BaseTrainer]  Epoch [77/120], MSELoss:126.7893\n",
      "[DEBUG   17:26:32]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 10.8268\n",
      "[INFO    17:26:34]  [BaseTrainer]  Epoch [78/120], MSELoss:126.6333\n",
      "[DEBUG   17:26:34]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 10.4820\n",
      "[INFO    17:26:36]  [BaseTrainer]  Epoch [79/120], MSELoss:124.9894\n",
      "[DEBUG   17:26:36]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.0664\n",
      "[DEBUG   17:26:36]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:26:38]  [BaseTrainer]  Epoch [80/120], MSELoss:124.4546\n",
      "[DEBUG   17:26:38]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.6157\n",
      "[DEBUG   17:26:38]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:26:39]  [BaseTrainer]  Epoch [81/120], MSELoss:123.0114\n",
      "[DEBUG   17:26:40]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.7284\n",
      "[DEBUG   17:26:40]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:26:41]  [BaseTrainer]  Epoch [82/120], MSELoss:114.4214\n",
      "[DEBUG   17:26:42]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 10.6464\n",
      "[DEBUG   17:26:42]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:26:43]  [BaseTrainer]  Epoch [83/120], MSELoss:127.2036\n",
      "[DEBUG   17:26:44]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 13.4617\n",
      "[DEBUG   17:26:44]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:26:46]  [BaseTrainer]  Epoch [84/120], MSELoss:120.1328\n",
      "[DEBUG   17:26:46]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.0228\n",
      "[DEBUG   17:26:46]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[INFO    17:26:47]  [BaseTrainer]  Epoch [85/120], MSELoss:110.3241\n",
      "[DEBUG   17:26:48]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.7667\n",
      "[INFO    17:26:49]  [BaseTrainer]  Epoch [86/120], MSELoss:109.8231\n",
      "[DEBUG   17:26:50]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 12.2468\n",
      "[DEBUG   17:26:50]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:26:51]  [BaseTrainer]  Epoch [87/120], MSELoss:115.0595\n",
      "[DEBUG   17:26:52]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.1245\n",
      "[DEBUG   17:26:52]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:26:53]  [BaseTrainer]  Epoch [88/120], MSELoss:112.8423\n",
      "[DEBUG   17:26:54]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.6269\n",
      "[INFO    17:26:55]  [BaseTrainer]  Epoch [89/120], MSELoss:106.3696\n",
      "[DEBUG   17:26:56]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 10.7620\n",
      "[DEBUG   17:26:56]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:26:57]  [BaseTrainer]  Epoch [90/120], MSELoss:100.8079\n",
      "[DEBUG   17:26:58]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.1957\n",
      "[INFO    17:26:59]  [BaseTrainer]  Epoch [91/120], MSELoss:101.3818\n",
      "[DEBUG   17:27:00]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.4537\n",
      "[DEBUG   17:27:00]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:27:01]  [BaseTrainer]  Epoch [92/120], MSELoss:100.8218\n",
      "[DEBUG   17:27:02]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 10.8954\n",
      "[DEBUG   17:27:02]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:27:03]  [BaseTrainer]  Epoch [93/120], MSELoss:99.3636\n",
      "[DEBUG   17:27:04]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.5477\n",
      "[DEBUG   17:27:04]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:27:05]  [BaseTrainer]  Epoch [94/120], MSELoss:102.1934\n",
      "[DEBUG   17:27:06]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.1458\n",
      "[DEBUG   17:27:06]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:27:07]  [BaseTrainer]  Epoch [95/120], MSELoss:97.5470\n",
      "[DEBUG   17:27:08]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.2102\n",
      "[DEBUG   17:27:08]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:27:09]  [BaseTrainer]  Epoch [96/120], MSELoss:94.0045\n",
      "[DEBUG   17:27:10]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.5202\n",
      "[DEBUG   17:27:10]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[INFO    17:27:11]  [BaseTrainer]  Epoch [97/120], MSELoss:88.1154\n",
      "[DEBUG   17:27:12]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.0933\n",
      "[INFO    17:27:13]  [BaseTrainer]  Epoch [98/120], MSELoss:91.2355\n",
      "[DEBUG   17:27:14]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.3159\n",
      "[DEBUG   17:27:14]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:27:15]  [BaseTrainer]  Epoch [99/120], MSELoss:99.5166\n",
      "[DEBUG   17:27:16]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.4671\n",
      "[DEBUG   17:27:16]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:27:17]  [BaseTrainer]  Epoch [100/120], MSELoss:88.1475\n",
      "[DEBUG   17:27:18]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.9966\n",
      "[DEBUG   17:27:18]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:27:19]  [BaseTrainer]  Epoch [101/120], MSELoss:115.6239\n",
      "[DEBUG   17:27:20]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.1512\n",
      "[DEBUG   17:27:20]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:27:21]  [BaseTrainer]  Epoch [102/120], MSELoss:91.4209\n",
      "[DEBUG   17:27:22]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 10.3231\n",
      "[DEBUG   17:27:22]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:27:23]  [BaseTrainer]  Epoch [103/120], MSELoss:89.4802\n",
      "[DEBUG   17:27:24]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 11.2440\n",
      "[DEBUG   17:27:24]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[INFO    17:27:25]  [BaseTrainer]  Epoch [104/120], MSELoss:84.1680\n",
      "[DEBUG   17:27:26]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.1604\n",
      "[INFO    17:27:27]  [BaseTrainer]  Epoch [105/120], MSELoss:78.8316\n",
      "[DEBUG   17:27:28]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.4352\n",
      "[DEBUG   17:27:28]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:27:29]  [BaseTrainer]  Epoch [106/120], MSELoss:78.0654\n",
      "[DEBUG   17:27:30]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.9654\n",
      "[DEBUG   17:27:30]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:27:31]  [BaseTrainer]  Epoch [107/120], MSELoss:76.5014\n",
      "[DEBUG   17:27:32]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.6430\n",
      "[DEBUG   17:27:32]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:27:33]  [BaseTrainer]  Epoch [108/120], MSELoss:79.7786\n",
      "[DEBUG   17:27:34]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.9071\n",
      "[DEBUG   17:27:34]  [EarlyStopping]  No improvement for [4/20] epochs\n",
      "[INFO    17:27:35]  [BaseTrainer]  Epoch [109/120], MSELoss:73.0389\n",
      "[DEBUG   17:27:36]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.7045\n",
      "[DEBUG   17:27:36]  [EarlyStopping]  No improvement for [5/20] epochs\n",
      "[INFO    17:27:37]  [BaseTrainer]  Epoch [110/120], MSELoss:70.7184\n",
      "[DEBUG   17:27:37]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.9574\n",
      "[DEBUG   17:27:37]  [EarlyStopping]  No improvement for [6/20] epochs\n",
      "[INFO    17:27:39]  [BaseTrainer]  Epoch [111/120], MSELoss:72.7295\n",
      "[DEBUG   17:27:39]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.2496\n",
      "[DEBUG   17:27:39]  [EarlyStopping]  No improvement for [7/20] epochs\n",
      "[INFO    17:27:40]  [BaseTrainer]  Epoch [112/120], MSELoss:78.0153\n",
      "[DEBUG   17:27:40]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 8.9923\n",
      "[DEBUG   17:27:40]  [EarlyStopping]  No improvement for [8/20] epochs\n",
      "[INFO    17:27:42]  [BaseTrainer]  Epoch [113/120], MSELoss:69.4905\n",
      "[DEBUG   17:27:42]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 9.6762\n",
      "[DEBUG   17:27:42]  [EarlyStopping]  No improvement for [9/20] epochs\n",
      "[INFO    17:27:43]  [BaseTrainer]  Epoch [114/120], MSELoss:68.9646\n",
      "[DEBUG   17:27:44]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 7.4032\n",
      "[INFO    17:27:45]  [BaseTrainer]  Epoch [115/120], MSELoss:73.9972\n",
      "[DEBUG   17:27:45]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 7.5972\n",
      "[DEBUG   17:27:45]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:27:46]  [BaseTrainer]  Epoch [116/120], MSELoss:66.3180\n",
      "[DEBUG   17:27:47]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 7.2208\n",
      "[INFO    17:27:48]  [BaseTrainer]  Epoch [117/120], MSELoss:64.2537\n",
      "[DEBUG   17:27:48]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 7.6113\n",
      "[DEBUG   17:27:48]  [EarlyStopping]  No improvement for [1/20] epochs\n",
      "[INFO    17:27:49]  [BaseTrainer]  Epoch [118/120], MSELoss:66.3413\n",
      "[DEBUG   17:27:50]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 7.4051\n",
      "[DEBUG   17:27:50]  [EarlyStopping]  No improvement for [2/20] epochs\n",
      "[INFO    17:27:51]  [BaseTrainer]  Epoch [119/120], MSELoss:74.3086\n",
      "[DEBUG   17:27:51]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 7.8513\n",
      "[DEBUG   17:27:51]  [EarlyStopping]  No improvement for [3/20] epochs\n",
      "[INFO    17:27:53]  [BaseTrainer]  Epoch [120/120], MSELoss:67.6764\n",
      "[DEBUG   17:27:53]  [EarlyStopping]  On the validation set FD001_train, the RMSE is 6.9411\n",
      "[WARNING 17:27:53]  [CheckGradients] output_linear.bias gradient is very large: 1.09e+03\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 160x96 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[DEBUG   17:27:53]  [Cache]  Generating cache file: .\\cache\\CMAPSS_model.pkl\n",
      "[DEBUG   17:27:53]  [Cache]  Generated cache file: .\\cache\\CMAPSS_model.pkl\n"
     ]
    }
   ],
   "source": [
    "model = Cache.load('CMAPSS_model', cache_model)\n",
    "if model is None:\n",
    "    model = TransformerEncoderModel(14, 1)\n",
    "\n",
    "    # 开始训练\n",
    "    loss_history_dicts = trainer(model=model, train_set=train_set)\n",
    "    Plotter.SIZE = (4, 2.4)\n",
    "    Plotter.loss(loss_history_dicts)\n",
    "    Cache.save(model, 'CMAPSS_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08d219e-929f-45a6-9db5-f55a48dbedf1",
   "metadata": {},
   "source": [
    "# 配置评价指标与评价器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f16fa489-dddf-4151-b962-62626fb5457a",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator = Evaluator(MAE(), MSE(), RMSE(), PercentError(), PHM2012Score(), PHM2008Score())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b04ba50-ae4a-4b72-86b3-c5ae450ba8b0",
   "metadata": {},
   "source": [
    "# 仅测试最后一个时间窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "74f6f8cc-ef48-4a66-87e2-135932a1bd9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:27:53]  \n",
      "[Evaluator]  Evaluation of last sample:\n",
      "                    MAE        MSE     RMSE PercentError PHM2012Score PHM2008Score\n",
      "FD001_test_1     9.6379    92.8893   9.6379       -8.61%       0.3033       1.6216\n",
      "FD001_test_2    27.3118   745.9368  27.3118      -27.87%       0.0210      14.3511\n",
      "FD001_test_3    26.9344   725.4608  26.9344       39.04%       0.2585       6.9397\n",
      "FD001_test_4     5.0500    25.5030   5.0500       -6.16%       0.4258       0.6570\n",
      "FD001_test_5    20.3956   415.9820  20.3956       22.41%       0.4599       3.8013\n",
      "FD001_test_6    34.0195  1157.3244  34.0195      -36.58%       0.0063      29.0225\n",
      "FD001_test_7    10.6723   113.8990  10.6723      -11.73%       0.1967       1.9073\n",
      "FD001_test_8    20.3683   414.8677  20.3683       21.44%       0.4757       3.7913\n",
      "FD001_test_9    15.7529   248.1553  15.7529      -14.19%       0.1398       3.8322\n",
      "FD001_test_10   13.8170   190.9085  13.8170      -14.39%       0.1360       2.9817\n",
      "FD001_test_11    5.1541    26.5648   5.1541        5.31%       0.8318       0.4866\n",
      "FD001_test_12    2.2274     4.9612   2.2274       -1.80%       0.7796       0.2495\n",
      "FD001_test_13   14.9181   222.5483  14.9181       15.70%       0.5803       2.1504\n",
      "FD001_test_14   15.2233   231.7502  15.2233      -14.23%       0.1391       3.5829\n",
      "FD001_test_15   27.2552   742.8457  27.2552      -32.84%       0.0105      14.2643\n",
      "FD001_test_16   14.4017   207.4086  14.4017      -17.14%       0.0928       3.2214\n",
      "FD001_test_17    0.8865     0.7859   0.8865       -1.77%       0.7821       0.0927\n",
      "FD001_test_18    1.8818     3.5411   1.8818       -6.72%       0.3939       0.2070\n",
      "FD001_test_19   33.1394  1098.2201  33.1394      -38.09%       0.0051      26.4932\n",
      "FD001_test_20    1.8041     3.2549   1.8041      -11.28%       0.2095       0.1977\n",
      "FD001_test_21    5.3889    29.0403   5.3889       -9.45%       0.2696       0.7141\n",
      "FD001_test_22   17.6382   311.1055  17.6382      -15.89%       0.1105       4.8347\n",
      "FD001_test_23   15.8005   249.6570  15.8005      -13.98%       0.1439       3.8552\n",
      "FD001_test_24    3.0527     9.3190   3.0527      -15.26%       0.1205       0.3570\n",
      "FD001_test_25    1.7492     3.0596   1.7492        1.35%       0.9544       0.1440\n",
      "FD001_test_26    2.8480     8.1110   2.8480       -2.39%       0.7176       0.3295\n",
      "FD001_test_27    7.8056    60.9276   7.8056      -11.83%       0.1941       1.1827\n",
      "FD001_test_28   11.4742   131.6563  11.4742      -11.83%       0.1940       2.1500\n",
      "FD001_test_29   12.1371   147.3096  12.1371       13.49%       0.6266       1.5437\n",
      "FD001_test_30   11.4405   130.8859  11.4405       -9.95%       0.2518       2.1395\n",
      "FD001_test_31    1.0288     1.0585   1.0288      -12.86%       0.1682       0.1084\n",
      "FD001_test_32    4.3951    19.3168   4.3951       -9.16%       0.2810       0.5519\n",
      "FD001_test_33   22.3794   500.8373  22.3794      -21.11%       0.0536       8.3740\n",
      "FD001_test_34    2.4876     6.1883   2.4876      -35.54%       0.0073       0.2824\n",
      "FD001_test_35    2.7236     7.4177   2.7236       24.76%       0.4240       0.2331\n",
      "FD001_test_36    3.5907    12.8928   3.5907      -18.90%       0.0728       0.4320\n",
      "FD001_test_37    6.4301    41.3464   6.4301      -30.62%       0.0143       0.9022\n",
      "FD001_test_38    4.3062    18.5432   4.3062        8.61%       0.7419       0.3927\n",
      "FD001_test_39    3.7341    13.9437   3.7341        2.87%       0.9052       0.3327\n",
      "FD001_test_40    1.2166     1.4801   1.2166       -4.34%       0.5475       0.1294\n",
      "FD001_test_41   12.7265   161.9627  12.7265      -70.70%       0.0001       2.5703\n",
      "FD001_test_42    3.7494    14.0578   3.7494      -37.49%       0.0055       0.4549\n",
      "FD001_test_43   10.4533   109.2710  10.4533      -17.72%       0.0858       1.8443\n",
      "FD001_test_44   17.0364   290.2389  17.0364      -15.63%       0.1146       4.4939\n",
      "FD001_test_45   46.2729  2141.1823  46.2729       40.59%       0.2449      34.1440\n",
      "FD001_test_46    2.8761     8.2720   2.8761        6.12%       0.8089       0.2476\n",
      "FD001_test_47    7.5895    57.6009   7.5895        5.84%       0.8168       0.7929\n",
      "FD001_test_48   24.6414   607.2006  24.6414      -26.78%       0.0244      10.7534\n",
      "FD001_test_49    0.1176     0.0138   0.1176        0.56%       0.9808       0.0091\n",
      "FD001_test_50   19.6433   385.8582  19.6433      -24.86%       0.0318       6.1301\n",
      "FD001_test_51   13.2308   175.0546  13.2308      -11.61%       0.2001       2.7550\n",
      "FD001_test_52    0.7398     0.5473   0.7398       -2.55%       0.7021       0.0768\n",
      "FD001_test_53    5.3647    28.7796   5.3647      -20.63%       0.0572       0.7100\n",
      "FD001_test_54   29.6880   881.3798  29.6880      -30.61%       0.0144      18.4686\n",
      "FD001_test_55   20.3267   413.1753  20.3267       15.64%       0.5816       3.7760\n",
      "FD001_test_56    0.3222     0.1038   0.3222        2.15%       0.9283       0.0251\n",
      "FD001_test_57   14.5400   211.4114  14.5400      -14.12%       0.1413       3.2802\n",
      "FD001_test_58    8.9067    79.3292   8.9067      -24.07%       0.0355       1.4368\n",
      "FD001_test_59   11.3895   129.7215  11.3895       -9.99%       0.2503       2.1235\n",
      "FD001_test_60   14.3822   206.8471  14.3822       14.38%       0.6075       2.0232\n",
      "FD001_test_61    1.6050     2.5760   1.6050       -7.64%       0.3466       0.1741\n",
      "FD001_test_62   11.6864   136.5712  11.6864       21.64%       0.4724       1.4570\n",
      "FD001_test_63   12.9694   168.2063  12.9694       18.01%       0.5356       1.7119\n",
      "FD001_test_64    3.0304     9.1836   3.0304       10.82%       0.6872       0.2625\n",
      "FD001_test_65    0.2104     0.0443   0.2104       -0.16%       0.9775       0.0213\n",
      "FD001_test_66    3.0246     9.1482   3.0246      -21.60%       0.0500       0.3532\n",
      "FD001_test_67   33.0303  1091.0015  33.0303      -42.90%       0.0026      26.1949\n",
      "FD001_test_68    3.3008    10.8951   3.3008       41.26%       0.2393       0.2891\n",
      "FD001_test_69    7.3621    54.2011   7.3621       -6.08%       0.4302       1.0880\n",
      "FD001_test_70   17.4862   305.7655  17.4862       18.60%       0.5248       2.8385\n",
      "FD001_test_71   30.1169   907.0266  30.1169       25.52%       0.4129       9.1420\n",
      "FD001_test_72   17.2261   296.7368  17.2261      -34.45%       0.0084       4.5991\n",
      "FD001_test_73   12.1511   147.6492  12.1511        9.35%       0.7233       1.5464\n",
      "FD001_test_74   23.1434   535.6164  23.1434       18.37%       0.5291       4.9314\n",
      "FD001_test_75   34.1852  1168.6271  34.1852       30.25%       0.3505      12.8686\n",
      "FD001_test_76    5.5483    30.7840   5.5483       55.48%       0.1462       0.5323\n",
      "FD001_test_77    0.2215     0.0490   0.2215        0.65%       0.9777       0.0172\n",
      "FD001_test_78   16.5271   273.1460  16.5271      -15.45%       0.1175       4.2211\n",
      "FD001_test_79    9.0958    82.7345   9.0958      -14.44%       0.1351       1.4833\n",
      "FD001_test_80   21.2999   453.6853  21.2999      -23.67%       0.0376       7.4148\n",
      "FD001_test_81    3.5839    12.8446   3.5839       44.80%       0.2117       0.3174\n",
      "FD001_test_82    0.3016     0.0910   0.3016        3.35%       0.8903       0.0235\n",
      "FD001_test_83    1.8068     3.2646   1.8068        1.39%       0.9530       0.1491\n",
      "FD001_test_84    9.5714    91.6122   9.5714      -16.50%       0.1015       1.6042\n",
      "FD001_test_85    4.5088    20.3289   4.5088       -3.82%       0.5888       0.5697\n",
      "FD001_test_86   34.8794  1216.5754  34.8794      -39.19%       0.0044      31.7186\n",
      "FD001_test_87   11.4345   130.7482  11.4345       -9.86%       0.2550       2.1376\n",
      "FD001_test_88    5.6014    31.3753   5.6014        4.87%       0.8447       0.5386\n",
      "FD001_test_89    3.4440    11.8610   3.4440        2.65%       0.9123       0.3033\n",
      "FD001_test_90    2.5388     6.4455   2.5388       -9.07%       0.2845       0.2890\n",
      "FD001_test_91   16.7596   280.8842  16.7596       44.10%       0.2169       2.6299\n",
      "FD001_test_92    2.8501     8.1233   2.8501       14.25%       0.6102       0.2451\n",
      "FD001_test_93   31.1312   969.1546  31.1312       36.62%       0.2810       9.9650\n",
      "FD001_test_94    1.7987     3.2355   1.7987       -3.27%       0.6355       0.1971\n",
      "FD001_test_95   36.8554  1358.3177  36.8554       28.79%       0.3687      16.0309\n",
      "FD001_test_96   31.6586  1002.2674  31.6586       24.35%       0.4300      10.4190\n",
      "FD001_test_97    4.1677    17.3697   4.1677       -5.08%       0.4943       0.5171\n",
      "FD001_test_98   21.3387   455.3407  21.3387      -36.17%       0.0066       7.4475\n",
      "FD001_test_99   11.3612   129.0777  11.3612       -9.71%       0.2602       2.1147\n",
      "FD001_test_100   0.6821     0.4652   0.6821       -3.41%       0.6233       0.0706\n",
      "mean(global)    12.0597   257.1194  16.0349       -2.96%       0.3636     413.3910\n",
      "mean(group)     12.0597   257.1194  12.0597       -3.80%       0.3636       4.1339\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>PercentError</th>\n",
       "      <th>PHM2012Score</th>\n",
       "      <th>PHM2008Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FD001_test_1</th>\n",
       "      <td>9.6379</td>\n",
       "      <td>92.8893</td>\n",
       "      <td>9.6379</td>\n",
       "      <td>-8.61%</td>\n",
       "      <td>0.3033</td>\n",
       "      <td>1.6216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_2</th>\n",
       "      <td>27.3118</td>\n",
       "      <td>745.9368</td>\n",
       "      <td>27.3118</td>\n",
       "      <td>-27.87%</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>14.3511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_3</th>\n",
       "      <td>26.9344</td>\n",
       "      <td>725.4608</td>\n",
       "      <td>26.9344</td>\n",
       "      <td>39.04%</td>\n",
       "      <td>0.2585</td>\n",
       "      <td>6.9397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_4</th>\n",
       "      <td>5.0500</td>\n",
       "      <td>25.5030</td>\n",
       "      <td>5.0500</td>\n",
       "      <td>-6.16%</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>0.6570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_5</th>\n",
       "      <td>20.3956</td>\n",
       "      <td>415.9820</td>\n",
       "      <td>20.3956</td>\n",
       "      <td>22.41%</td>\n",
       "      <td>0.4599</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_98</th>\n",
       "      <td>21.3387</td>\n",
       "      <td>455.3407</td>\n",
       "      <td>21.3387</td>\n",
       "      <td>-36.17%</td>\n",
       "      <td>0.0066</td>\n",
       "      <td>7.4475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_99</th>\n",
       "      <td>11.3612</td>\n",
       "      <td>129.0777</td>\n",
       "      <td>11.3612</td>\n",
       "      <td>-9.71%</td>\n",
       "      <td>0.2602</td>\n",
       "      <td>2.1147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_100</th>\n",
       "      <td>0.6821</td>\n",
       "      <td>0.4652</td>\n",
       "      <td>0.6821</td>\n",
       "      <td>-3.41%</td>\n",
       "      <td>0.6233</td>\n",
       "      <td>0.0706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean(global)</th>\n",
       "      <td>12.0597</td>\n",
       "      <td>257.1194</td>\n",
       "      <td>16.0349</td>\n",
       "      <td>-2.96%</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>413.3910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean(group)</th>\n",
       "      <td>12.0597</td>\n",
       "      <td>257.1194</td>\n",
       "      <td>12.0597</td>\n",
       "      <td>-3.80%</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>4.1339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    MAE       MSE     RMSE PercentError PHM2012Score  \\\n",
       "FD001_test_1     9.6379   92.8893   9.6379       -8.61%       0.3033   \n",
       "FD001_test_2    27.3118  745.9368  27.3118      -27.87%       0.0210   \n",
       "FD001_test_3    26.9344  725.4608  26.9344       39.04%       0.2585   \n",
       "FD001_test_4     5.0500   25.5030   5.0500       -6.16%       0.4258   \n",
       "FD001_test_5    20.3956  415.9820  20.3956       22.41%       0.4599   \n",
       "...                 ...       ...      ...          ...          ...   \n",
       "FD001_test_98   21.3387  455.3407  21.3387      -36.17%       0.0066   \n",
       "FD001_test_99   11.3612  129.0777  11.3612       -9.71%       0.2602   \n",
       "FD001_test_100   0.6821    0.4652   0.6821       -3.41%       0.6233   \n",
       "mean(global)    12.0597  257.1194  16.0349       -2.96%       0.3636   \n",
       "mean(group)     12.0597  257.1194  12.0597       -3.80%       0.3636   \n",
       "\n",
       "               PHM2008Score  \n",
       "FD001_test_1         1.6216  \n",
       "FD001_test_2        14.3511  \n",
       "FD001_test_3         6.9397  \n",
       "FD001_test_4         0.6570  \n",
       "FD001_test_5         3.8013  \n",
       "...                     ...  \n",
       "FD001_test_98        7.4475  \n",
       "FD001_test_99        2.1147  \n",
       "FD001_test_100       0.0706  \n",
       "mean(global)       413.3910  \n",
       "mean(group)          4.1339  \n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_last_sample = tester(model, test_set_last_sample)\n",
    "evaluator(test_set_last_sample, result_last_sample, title='Evaluation of last sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cce476aa-4a1a-47a0-ad54-4c6020ca3d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 240x160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'RUL prediction result'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plotter.SIZE = (6, 4)\n",
    "Plotter.rul_ascending(test_set_last_sample, result_last_sample,\n",
    "                      is_scatter=False, label_x='Test Engine ID', label_y='RUL (cycle)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8849c05-fa93-48dc-bbda-0810c72a9cf2",
   "metadata": {},
   "source": [
    "# 测试所有时间窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3ed6d3ba-61cc-4d32-b1dd-9a6a4d933d13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO    17:27:54]  \n",
      "[Evaluator]  Evaluation of all sample:\n",
      "                    MAE        MSE     RMSE PercentError PHM2012Score PHM2008Score\n",
      "FD001_test_1    11.3505   131.7666  11.4790      -10.09%       0.2524       4.3141\n",
      "FD001_test_2    16.7381   326.0165  18.0559      -15.18%       0.1890     112.4092\n",
      "FD001_test_3    18.2364   527.7001  22.9717        0.68%       0.4527     875.3266\n",
      "FD001_test_4    12.0278   346.1278  18.6045       -9.17%       0.5544     794.7071\n",
      "FD001_test_5    10.4751   175.3800  13.2431        2.08%       0.6051     156.1480\n",
      "FD001_test_6     5.2694    78.7502   8.8741       -1.61%       0.7706     121.1735\n",
      "FD001_test_7     3.9365    39.0537   6.2493       -0.67%       0.8116      89.7536\n",
      "FD001_test_8     8.1629   173.9248  13.1881        2.64%       0.7437     378.1149\n",
      "FD001_test_9     6.6836    65.0692   8.0665        0.48%       0.7233      26.3220\n",
      "FD001_test_10   10.7523   258.7946  16.0871        7.25%       0.7520     513.0746\n",
      "FD001_test_11   13.9834   258.5199  16.0786        3.96%       0.5433     165.3863\n",
      "FD001_test_12    8.5160   274.7929  16.5769        6.54%       0.8408     853.1587\n",
      "FD001_test_13   11.0337   247.8060  15.7419        8.37%       0.7572     473.0523\n",
      "FD001_test_14   11.0856   141.3104  11.8874       -9.64%       0.3003      39.3453\n",
      "FD001_test_15   16.7673   451.5684  21.2501      -15.79%       0.3134     506.1556\n",
      "FD001_test_16    8.0233   125.8514  11.2183       -4.82%       0.5982     187.4075\n",
      "FD001_test_17   11.0341   227.9112  15.0967        7.04%       0.6332     358.8020\n",
      "FD001_test_18   16.5956   586.9430  24.2269      -19.10%       0.3415   11545.0689\n",
      "FD001_test_19    8.4006   179.1757  13.3857       -0.15%       0.7531     358.7833\n",
      "FD001_test_20    9.1837   163.4413  12.7844       -3.41%       0.5320     502.8494\n",
      "FD001_test_21    5.3895    60.3123   7.7661       -2.96%       0.6587     128.6294\n",
      "FD001_test_22   13.0675   179.1013  13.3829      -11.31%       0.2220      28.5025\n",
      "FD001_test_23    2.9427    18.3706   4.2861        0.53%       0.8790      37.9181\n",
      "FD001_test_24    5.4811    78.7160   8.8722       -4.04%       0.6229     240.4596\n",
      "FD001_test_25    5.1105    69.8986   8.3605        3.93%       0.8849      14.4428\n",
      "FD001_test_26    8.5061   145.9959  12.0829        6.46%       0.8080      72.1840\n",
      "FD001_test_27   11.4921   281.8864  16.7895       -7.97%       0.5297     773.8336\n",
      "FD001_test_28    6.3426    84.2530   9.1789        2.73%       0.8005     144.4540\n",
      "FD001_test_29   11.3253   245.0649  15.6545        7.05%       0.7210     387.8685\n",
      "FD001_test_30   24.2099   985.8227  31.3978       18.62%       0.5846    2693.5540\n",
      "FD001_test_31   11.2016   242.5263  15.5733        7.46%       0.5585     489.0233\n",
      "FD001_test_32    7.2490   115.7229  10.7575       -4.88%       0.5903     247.9553\n",
      "FD001_test_33   11.8414   175.1834  13.2357      -10.21%       0.3061      60.8924\n",
      "FD001_test_34    6.4032    95.0388   9.7488       -1.36%       0.6001     321.3887\n",
      "FD001_test_35   11.8760   327.6479  18.1010       10.97%       0.5981     710.2037\n",
      "FD001_test_36   11.8897   207.3649  14.4002      -15.82%       0.2131     369.8925\n",
      "FD001_test_37   27.8096   954.9503  30.9023      -41.82%       0.0155    3332.3082\n",
      "FD001_test_38   18.7685   640.7781  25.3136       -1.50%       0.4082    1611.9208\n",
      "FD001_test_39    1.6185     3.2688   1.8080        1.24%       0.9580       1.0789\n",
      "FD001_test_40   15.2986   355.6603  18.8590       -4.93%       0.3440     728.7437\n",
      "FD001_test_41   31.0598  1191.1775  34.5134      -48.15%       0.0116    5658.7941\n",
      "FD001_test_42   22.2271   831.9748  28.8440      -30.29%       0.1436    6895.3705\n",
      "FD001_test_43   15.7686   474.3775  21.7802       -1.04%       0.5364    1633.6708\n",
      "FD001_test_44    6.7791    69.2057   8.3190       -5.20%       0.5416      30.4167\n",
      "FD001_test_45   15.0870   433.4388  20.8192       11.62%       0.7066     722.6998\n",
      "FD001_test_46   11.9554   244.6870  15.6425        9.83%       0.6417     311.7703\n",
      "FD001_test_47   17.8008   568.9607  23.8529       13.69%       0.6730     352.2028\n",
      "FD001_test_48   12.1141   235.1654  15.3351       -9.29%       0.4283     211.2734\n",
      "FD001_test_49    8.2764   160.7721  12.6796        5.92%       0.7295     521.7855\n",
      "FD001_test_50   20.1836   492.2967  22.1878      -19.98%       0.1392     455.0931\n",
      "FD001_test_51   11.0867   255.5063  15.9846        8.12%       0.7606     341.1014\n",
      "FD001_test_52    8.9067   196.2146  14.0077       -7.89%       0.5051     772.7614\n",
      "FD001_test_53    8.4965   143.2159  11.9673       -3.55%       0.5355     350.8032\n",
      "FD001_test_54    6.2041   108.9736  10.4390       -3.59%       0.7195     184.7935\n",
      "FD001_test_55    4.2135    37.9913   6.1637        3.24%       0.8997      41.4297\n",
      "FD001_test_56   13.5812   250.0238  15.8121      -12.95%       0.2342     503.9346\n",
      "FD001_test_57    6.3661   117.7956  10.8534        2.12%       0.7860     201.4021\n",
      "FD001_test_58    8.4903   135.7314  11.6504       -3.47%       0.5705     375.9660\n",
      "FD001_test_59    3.4633    20.9026   4.5719        0.22%       0.8357      27.7716\n",
      "FD001_test_60   10.7850   283.9038  16.8494        8.56%       0.7767     460.8215\n",
      "FD001_test_61   14.7276   475.4713  21.8053      -14.55%       0.3959    9764.4780\n",
      "FD001_test_62    5.6342    72.2264   8.4986        4.17%       0.8156     163.6205\n",
      "FD001_test_63   12.0136   239.7739  15.4846        5.63%       0.6274     378.5531\n",
      "FD001_test_64   14.2758   371.1784  19.2660       -3.09%       0.4937    1840.3987\n",
      "FD001_test_65    3.5552    20.7988   4.5606        2.73%       0.9117      14.7556\n",
      "FD001_test_66    9.5394   147.5007  12.1450       -9.53%       0.3651     302.9805\n",
      "FD001_test_67   24.2318   783.9919  27.9999      -17.64%       0.2806     974.0046\n",
      "FD001_test_68   11.7661   199.1714  14.1128       -3.37%       0.4352     543.9220\n",
      "FD001_test_69    3.4893    43.1880   6.5718        0.66%       0.8398      14.8954\n",
      "FD001_test_70   10.3594   215.8578  14.6921        7.51%       0.7471     294.0929\n",
      "FD001_test_71   12.9259   278.3894  16.6850       10.10%       0.7295     112.8260\n",
      "FD001_test_72   20.4194   675.3032  25.9866      -18.14%       0.3246    3271.6105\n",
      "FD001_test_73    4.3903    33.1861   5.7607        3.38%       0.8937      38.9711\n",
      "FD001_test_74    9.0813   219.8469  14.8272        6.99%       0.8176     284.8902\n",
      "FD001_test_75   16.9655   409.2136  20.2290       13.32%       0.6564     258.6628\n",
      "FD001_test_76    6.5915    82.0601   9.0587       -1.32%       0.6165     223.5503\n",
      "FD001_test_77   13.6654   323.9675  17.9991       -1.51%       0.5121     859.8538\n",
      "FD001_test_78    5.8088    69.1580   8.3161       -3.69%       0.6633      50.0458\n",
      "FD001_test_79   20.1323   571.0636  23.8969      -19.51%       0.2222    1164.5156\n",
      "FD001_test_80    8.4352   114.4438  10.6978        4.50%       0.7331     141.2535\n",
      "FD001_test_81    9.5990   191.4696  13.8373        6.27%       0.6273     439.8342\n",
      "FD001_test_82   13.6958   322.5901  17.9608       -6.97%       0.3319     804.2765\n",
      "FD001_test_83    7.6470   133.1831  11.5405        5.88%       0.8344      68.5845\n",
      "FD001_test_84   17.5608   559.8473  23.6611       -3.15%       0.5195    3450.6957\n",
      "FD001_test_85    2.0544     5.9801   2.4454       -1.71%       0.7973       1.1968\n",
      "FD001_test_86   12.4776   332.0744  18.2229        4.59%       0.6870     429.9947\n",
      "FD001_test_87    5.8124    62.3438   7.8958        1.88%       0.7807      22.7120\n",
      "FD001_test_88   20.2602   698.9004  26.4367       15.96%       0.6315     394.5899\n",
      "FD001_test_89    5.4207    59.8550   7.7366        4.17%       0.8741     104.3254\n",
      "FD001_test_90    8.5356   110.8008  10.5262       -6.76%       0.4219     214.6591\n",
      "FD001_test_91   20.2706   783.4300  27.9898       18.48%       0.5737    4340.5448\n",
      "FD001_test_92   13.8217   310.0589  17.6085      -13.98%       0.3366     777.4700\n",
      "FD001_test_93   26.6810   965.7913  31.0772       21.31%       0.5224    2882.7741\n",
      "FD001_test_94   10.0459   224.4458  14.9815        3.62%       0.6555     338.8496\n",
      "FD001_test_95    7.3598   158.8349  12.6030        5.66%       0.8470     117.3985\n",
      "FD001_test_96    2.6827    24.1334   4.9126        2.06%       0.9356      24.6475\n",
      "FD001_test_97   10.6724   201.9917  14.2124        2.30%       0.6682     332.1447\n",
      "FD001_test_98   22.5013   788.6590  28.0831       -1.54%       0.3833    2040.6354\n",
      "FD001_test_99    2.2587    10.4289   3.2294        0.19%       0.8919      17.8089\n",
      "FD001_test_100   4.9696    50.8545   7.1312        1.91%       0.7483     120.9038\n",
      "mean(global)    11.4908   291.2295  17.0654        0.96%       0.5991   88132.3959\n",
      "mean(group)     11.4908   291.2295  15.6484       -0.26%       0.5991    1064.3706\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>PercentError</th>\n",
       "      <th>PHM2012Score</th>\n",
       "      <th>PHM2008Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FD001_test_1</th>\n",
       "      <td>11.3505</td>\n",
       "      <td>131.7666</td>\n",
       "      <td>11.4790</td>\n",
       "      <td>-10.09%</td>\n",
       "      <td>0.2524</td>\n",
       "      <td>4.3141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_2</th>\n",
       "      <td>16.7381</td>\n",
       "      <td>326.0165</td>\n",
       "      <td>18.0559</td>\n",
       "      <td>-15.18%</td>\n",
       "      <td>0.1890</td>\n",
       "      <td>112.4092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_3</th>\n",
       "      <td>18.2364</td>\n",
       "      <td>527.7001</td>\n",
       "      <td>22.9717</td>\n",
       "      <td>0.68%</td>\n",
       "      <td>0.4527</td>\n",
       "      <td>875.3266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_4</th>\n",
       "      <td>12.0278</td>\n",
       "      <td>346.1278</td>\n",
       "      <td>18.6045</td>\n",
       "      <td>-9.17%</td>\n",
       "      <td>0.5544</td>\n",
       "      <td>794.7071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_5</th>\n",
       "      <td>10.4751</td>\n",
       "      <td>175.3800</td>\n",
       "      <td>13.2431</td>\n",
       "      <td>2.08%</td>\n",
       "      <td>0.6051</td>\n",
       "      <td>156.1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_98</th>\n",
       "      <td>22.5013</td>\n",
       "      <td>788.6590</td>\n",
       "      <td>28.0831</td>\n",
       "      <td>-1.54%</td>\n",
       "      <td>0.3833</td>\n",
       "      <td>2040.6354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_99</th>\n",
       "      <td>2.2587</td>\n",
       "      <td>10.4289</td>\n",
       "      <td>3.2294</td>\n",
       "      <td>0.19%</td>\n",
       "      <td>0.8919</td>\n",
       "      <td>17.8089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FD001_test_100</th>\n",
       "      <td>4.9696</td>\n",
       "      <td>50.8545</td>\n",
       "      <td>7.1312</td>\n",
       "      <td>1.91%</td>\n",
       "      <td>0.7483</td>\n",
       "      <td>120.9038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean(global)</th>\n",
       "      <td>11.4908</td>\n",
       "      <td>291.2295</td>\n",
       "      <td>17.0654</td>\n",
       "      <td>0.96%</td>\n",
       "      <td>0.5991</td>\n",
       "      <td>88132.3959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean(group)</th>\n",
       "      <td>11.4908</td>\n",
       "      <td>291.2295</td>\n",
       "      <td>15.6484</td>\n",
       "      <td>-0.26%</td>\n",
       "      <td>0.5991</td>\n",
       "      <td>1064.3706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    MAE       MSE     RMSE PercentError PHM2012Score  \\\n",
       "FD001_test_1    11.3505  131.7666  11.4790      -10.09%       0.2524   \n",
       "FD001_test_2    16.7381  326.0165  18.0559      -15.18%       0.1890   \n",
       "FD001_test_3    18.2364  527.7001  22.9717        0.68%       0.4527   \n",
       "FD001_test_4    12.0278  346.1278  18.6045       -9.17%       0.5544   \n",
       "FD001_test_5    10.4751  175.3800  13.2431        2.08%       0.6051   \n",
       "...                 ...       ...      ...          ...          ...   \n",
       "FD001_test_98   22.5013  788.6590  28.0831       -1.54%       0.3833   \n",
       "FD001_test_99    2.2587   10.4289   3.2294        0.19%       0.8919   \n",
       "FD001_test_100   4.9696   50.8545   7.1312        1.91%       0.7483   \n",
       "mean(global)    11.4908  291.2295  17.0654        0.96%       0.5991   \n",
       "mean(group)     11.4908  291.2295  15.6484       -0.26%       0.5991   \n",
       "\n",
       "               PHM2008Score  \n",
       "FD001_test_1         4.3141  \n",
       "FD001_test_2       112.4092  \n",
       "FD001_test_3       875.3266  \n",
       "FD001_test_4       794.7071  \n",
       "FD001_test_5       156.1480  \n",
       "...                     ...  \n",
       "FD001_test_98     2040.6354  \n",
       "FD001_test_99       17.8089  \n",
       "FD001_test_100     120.9038  \n",
       "mean(global)     88132.3959  \n",
       "mean(group)       1064.3706  \n",
       "\n",
       "[102 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_all_sample = tester(model, test_set_all_sample)\n",
    "evaluator(test_set_all_sample, result_all_sample, title='Evaluation of all sample')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "695220cf-8552-4f55-8671-5df89a0a4537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1520x880 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'default'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plotter.SIZE = (3.8, 2.2)\n",
    "Plotter.rul_end2end(test_set_all_sample, result_all_sample,\n",
    "                    is_scatter=False, label_x='Time (cycle)', label_y='RUL (cycle)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235d367a-0bbf-4e34-bd74-9497c06b8d94",
   "metadata": {},
   "source": [
    "# 仅绘图某个发动机预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "97655d6e-0a1e-4e29-a510-fa216f4d6b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 240x160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'default'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Plotter.SIZE = (6, 4)\n",
    "Plotter.rul_end2end(test_set_all_sample.get('FD001_test_76'), result_all_sample.get('FD001_test_76'),\n",
    "                    is_scatter=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e9f3a7b-22a3-4069-9db5-83c8361accce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
